SoroushVahidi/scidocs huggingface dataset:
Anaphoric Annotation in the ARRAU Corpus
(2008)

Arrau is a new corpus annotated for anaphoric relations, with information about agreement and explicit representation of multiple antecedents for ambiguous anaphoric expressions and discourse antecedents for expressions which refer to abstract entities such as events, actions and plans. The corpus contains texts from different genres: task-oriented dialogues from the Trains-91 and Trains-93 corpus, narratives from the English Pear Stories corpus, newspaper articles from the Wall Street Journal portion of the Penn Treebank, and mixed text from the Gnome corpus.
Index Selection for OLAP
(1997)

On-line analytical processing (OLAP) is a recent and important application of database systems. Typically, OLAP data is presented as a multidimensional \data cube." OLAP queries are complex and can take many hours or even days to run, if executed directly on the raw data. The most common method of reducing execution time is to precompute some of the queries into summary tables (subcubes of the data cube) and then to build indexes on these summary tables. In most commercial OLAP systems today, the summary tables that are to be precomputed are picked rst, followed by the selection of the appropriate indexes on them. A trial-and-error approach is used to divide the space available between the summary tables and the indexes. This two-step process can perform very poorly. Since both summary tables and indexes consume the same resource |space | their selection should be done together for the most e cient use of space. In this paper, we give algorithms that automate the selection of summary tables and indexes. In particular, we present a family of algorithms of increasing time complexities, and prove strong performance bounds for them. The algorithms with higher complexities have better performance bounds. However, the increase in the performance bound is diminishing, and we show that an algorithm of moderate complexity can perform fairly close to the optimal. This work was supported by NSF grant IRI{92{23405, ARO grant DAAH04{95{1{0192, and Air Force Contract F33615{93{ 1{1339. Present address of V. Harinarayan and A. Rajaraman: Junglee Corp., Palo Alto, CA.
Event-Centric Clustering of News Articles
(2013)

Entertainity AB plans to build a news service to provide news to end-users in an innovative way. The service must include a way to automatically group series of news from different sources and publications, based on the stories they are covering. This thesis include three contributions: a survey of known clustering methods, an evaluation of human versus human results when grouping news articles in an event-centric manner, and last an evaluation of an incremental clustering algorithm to see if it is possible to consider a reduced input size and still get a sufficient result. The conclusions are that the result of the human evaluation indicates that users are different enough to warrant a need to take that into account when evaluating algorithms. It is also important that this difference is considered when conducting cluster analysis to avoid overfitting. The evaluation of an incremental event-centric algorithm shows it is desirable to adjust the similarity threshold, depending on what result one want. When running tests with different input sizes, the result implies that a short summary of a news article is a natural feature selection when performing cluster analysis.
Can Invisible Watermarks Resolve Rightful Ownerships?
(1997)

Digital watermarks have been proposed in recent literature as the means for copyright protection of multimedia data. In this paper we address the capability of invisible watermarking schemes to resolve copyright ownerships. We will show that rightful ownerships cannot be resolved by current watermarking schemes alone. In addition, in the absence of standardization of watermarking procedures, anyone can claim ownership of any watermarked image. Specifically, we provide counterfeit watermarking schemes that can be performed on a watermarked image to allow multiple claims of rightful ownerships. We also propose non-invertible watermarking schemes in this paper and discuss in general the usefulness of digital watermarks in identifying the rightful copyright owners. The results, coupled with the recent attacks on some image watermarks, further imply that we have to carefully re-think our approaches to invisible watermarking of images, and re-evaluate the promises, applications and limitations of such digital means of copyright protection.
Reflection from layered surfaces due to subsurface scattering
(1993)

The reflection of light from most materials consists of two major terms: the specular and the diffuse. Specular reflection may be modeled from first principles by considering a rough surface consisting of perfect reflectors, or micro-facets. Diffuse reflection is generally considered to result from multiple scattering either from a rough surface or from within a layer near the surface. Accounting for diffuse reflection by Lambert’s Cosine Law, as is universally done in computer graphics, is not a physical theory based on first principles. This paper presents a model for subsurface scattering in layered surfaces in terms of one-dimensional linear transport theory. We derive explicit formulas for backscattering and transmission that can be directly incorporated in most rendering systems, and a general Monte Carlo method that is easily added to a ray tracer. This model is particularly appropriate for common layered materials appearing in nature, such as biological tissues (e.g. skin, leaves, etc.) or inorganic materials (e.g. snow, sand, paint, varnished or dusty surfaces). As an application of the model, we simulate the appearance of a face and a cluster of leaves from experimental data describing their layer properties. CR
Semantic mapping for mobile robotics tasks: A survey
(2015)

The evolution of contemporary mobile robotics has given thrust to a series of additional conjunct technologies. Of such is the semantic mapping, which provides an abstraction of space and a means for human-robot communication. The recent introduction and evolution of semantic mapping motivated this survey, in which an explicit analysis of the existing methods is sought. The several algorithms are categorized according to their primary characteristics, namely scalability, inference model, temporal coherence and topological map usage. The applications involving semantic maps are also outlined in the work at hand, emphasizing on human interaction, knowledge representation and planning. The existence of public available validation datasets and benchmarking, suitable for the evaluation of semantic mapping techniques is also discussed in detail. Last, an attempt to address open issues and questions is also made.
Power Electronics Technologies for Railway Vehicles
(2007)

This paper describes the current range of advanced IGBT propulsion inverters used in railways from light rail vehicles to high speed trains & locomotives. The second part of the paper describes future trends in IGBT railway propulsion drives. One such trend is the concept of power integration which leads to weight, volume and cost reduction . Finally, for systems with an AC input supply, a concept for reducing the weight of the main input transformer is also described. This uses a configuration of resonant converter and a medium frequency transformer , the so called e-transformer, and could be particularly targeted for 15 kV, 16.7 Hz supplied systems.
Modeling ITIL Business Motivation Model in ArchiMate
(2013)

According to Enterprise Architecture (EA) approaches, organizations have motivational concepts that are used to model the motivations that underlie its design or change, which represent the organization’s Business Motivation Model (BMM). Likewise, this BMM is also present in the organizations who provide IT services. ITIL has become a reference for IT service providers, but is commonly modeled as a process-oriented approach to IT Service Management, often disregarding the remaining EA domains or its motivational elements. Conversely, we believe that like EA, ITIL has an important motivation model that should be formally represented.
The use of unlabeled data to improve supervised learning for text summarization
(2002)

With the huge amount of information available electronically, there is an increasing demand for automatic text summarization systems. The use of machine learning techniques for this task allows one to adapt summaries to the user needs and to the corpus characteristics. These desirable properties have motivated an increasing amount of work in this field over the last few years. Most approaches attempt to generate summaries by extracting sentence segments and adopt the supervised learning paradigm which requires to label documents at the text span level. This is a costly process, which puts strong limitations on the applicability of these methods. We investigate here the use of semi-supervised algorithms for summarization. These techniques make use of few labeled data together with a larger amount of unlabeled data. We propose new semi-supervised algorithms for training classification models for text summarization. We analyze their performances on two data sets - the Reuters news-wire corpus and the Computation and Language (cmp_lg) collection of TIPSTER SUMMAC. We perform comparisons with a baseline - non learning - system, and a reference trainable summarizer system.
Neither Quick Nor Proper - Evaluation of QuickProp for Learning Deep Neural Networks
(2016)

Neural networks and especially convolutional neural networks are of great interest in current computer vision research. However, many techniques, extensions, and modifications have been published in the past, which are not yet used by current approaches. In this paper, we study the application of a method called QuickProp for training of deep neural networks. In particular, we apply QuickProp during learning and testing of fully convolutional networks for the task of semantic segmentation. We compare QuickProp empirically with gradient descent, which is the current standard method. Experiments suggest that QuickProp can not compete with standard gradient descent techniques for complex computer vision tasks like semantic segmentation.
Triple-Wideband Open-Slot Antenna for the LTE Metal-Framed Tablet device
(2015)

An open-slot antenna with a low profile of 7 mm to provide a triple-wideband LTE operation in the metal-framed tablet device is presented. With the low profile, the antenna can fit in the narrow region between the metal frame and the display panel of the metal-framed tablet device. The triple-wideband operation covers 698-960 MHz (low band), 1710-2690 MHz (middle band), and 3400-3800 MHz (high band). The antenna mainly has a rectangular open slot and an L-shaped metal strip embedded therein to provide two open slots of different resonant paths. Using a microstrip feedline having a step-shaped section across the two open slots, good excitation of multiple slot resonant modes is obtained. Details of the proposed antenna are presented.
Static analysis tools as early indicators of pre-release defect density
(2005)

During software development it is helpful to obtain early estimates of the defect density of software components. Such estimates identify fault-prone areas of code requiring further testing. We present an empirical approach for the early prediction of pre-release defect density based on the defects found using static analysis tools. The defects identified by two different static analysis tools are used to fit and predict the actual pre-release defect density for Windows Server 2003. We show that there exists a strong positive correlation between the static analysis defect density and the pre-release defect density determined by testing. Further, the predicted pre-release defect density and the actual pre-release defect density are strongly correlated at a high degree of statistical significance. Discriminant analysis shows that the results of static analysis tools can be used to separate high and low quality components with an overall classification rate of 82.91%.
LTSG: Latent Topical Skip-Gram for Mutually Improving Topic Model and Vector Representations
(2018)

Topic models have been widely used in discovering latent topics which are shared across documents in text mining. Vector representations, word embeddings and topic embeddings, map words and topics into a low-dimensional and dense real-value vector space, which have obtained high performance in NLP tasks. However, most of the existing models assume the results trained by one of them are perfect correct and used as prior knowledge for improving the other model. Some other models use the information trained from external large corpus to help improving smaller corpus. In this paper, we aim to build such an algorithm framework that makes topic models and vector representations mutually improve each other within the same corpus. An EM-style algorithm framework is employed to iteratively optimize both topic model and vector representations. Experimental results show that our model outperforms state-of-the-art methods on various NLP tasks.
Hierarchical Classification of Ten Skin Lesion Classes
(2015)

This paper presents a hierarchical classification system based on the kNearest Neighbors (kNN) classifier for classification of ten different classes of Malignant and Benign skin lesions from color image data. Our key contribution is to focus on the ten most common classes of skin lesions. There are five malignant: Actinic Keratosis (AK), Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), Melanoma (MEL), Intraepithelial Carcinoma (IEC) and five benign: Melanocytic Nevus / Mole (ML), Seborrhoeic Keratosis (SK), Dermatofibroma (DF), Haemangioma (VASC), Pyogenic Granuloma (PYO). Moreover, we use only high resolution color images acquired using a standard camera (nondermoscopy). Our image dataset contains 1300 lesions belonging to ten classes (45 AK, 239 BCC, 331 ML, 88 SCC, 257 SK, 76 MEL, 65 DF, 97 VASC, 24 PYO and 78 IEC).
Visual saliency detection based on Bayesian model
(2011)

Image saliency detection is very useful in many computer vision tasks while it still remains a challenging problem. In this paper, we propose a new computational saliency detection model which is implemented with a coarse to fine strategy under the Bayesian framework. First, saliency points are applied to get a coarse location of the saliency region. And then, based on the rough region, we compute a prior map for the Bayesian model to achieve the final saliency map. Experimental results on a public available dataset show the effectiveness of the proposed prior map and the strength of our saliency map compared with several previous method.
A 0.65 THz Focal-Plane Array in a Quarter-Micron CMOS Process Technology
(2009)

A focal-plane array (FPA) for room-temperature detection of 0.65-THz radiation has been fully integrated in a low-cost 0.25 mum CMOS process technology. The circuit architecture is based on the principle of distributed resistive self-mixing and facilitates broadband direct detection well beyond the cutoff frequency of the technology. The 3 timesZ 5 pixel array consists of differential on-chip patch antennas, NMOS direct detectors, and integrated 43-dB voltage amplifiers. At 0.65 THz the FPA achieves a responsivity (Rv) of 80 kV/W and a noise equivalent power (NEP) of 300 pW/ radic{Hz}. Active multi-pixel imaging of postal envelopes demonstrates the FPAs potential for future cost-effective terahertz imaging solutions.
Regulation of wound healing by growth factors and cytokines.
(2003)

Cutaneous wound healing is a complex process involving blood clotting, inflammation, new tissue formation, and finally tissue remodeling. It is well described at the histological level, but the genes that regulate skin repair have only partially been identified. Many experimental and clinical studies have demonstrated varied, but in most cases beneficial, effects of exogenous growth factors on the healing process. However, the roles played by endogenous growth factors have remained largely unclear. Initial approaches at addressing this question focused on the expression analysis of various growth factors, cytokines, and their receptors in different wound models, with first functional data being obtained by applying neutralizing antibodies to wounds. During the past few years, the availability of genetically modified mice has allowed elucidation of the function of various genes in the healing process, and these studies have shed light onto the role of growth factors, cytokines, and their downstream effectors in wound repair. This review summarizes the results of expression studies that have been performed in rodents, pigs, and humans to localize growth factors and their receptors in skin wounds. Most importantly, we also report on genetic studies addressing the functions of endogenous growth factors in the wound repair process.
Traffic accident prediction using 3-D model-based vehicle tracking
(2004)

Intelligent visual surveillance for road vehicles is the key to developing autonomous intelligent traffic systems. Recently, traffic incident detection employing computer vision and image processing has attracted much attention. In this paper, a probabilistic model for predicting traffic accidents using three-dimensional (3-D) model-based vehicle tracking is proposed. Sample data including motion trajectories are first obtained by 3-D model-based vehicle tracking. A fuzzy self-organizing neural network algorithm is then applied to learn activity patterns from the sample trajectories. Finally, vehicle activity is predicted by locating and matching each partial trajectory with the learned activity patterns, and the occurrence probability of a traffic accident is determined. Experiments show the effectiveness of the proposed algorithms.
DETERMINING TRENDS IN GLOBAL CRIME AND JUSTICE : AN OVERVIEW OF RESULTS FROM THE UNITED NATIONS SURVEYS OF CRIME TRENDS AND OPERATIONS OF CRIMINAL JUSTICE SYSTEMS
(2004)

Effectively measuring comparative developments in crime and justice trends from a global perspective remains a key challenge for international policy makers. The ability to compare crime levels across countries enables policy makers to determine where interventions should occur and improves understanding of the key causes of crime in different societies across the globe. Nevertheless, there are significant challenges to comparative work in the field of criminal justice, not least of which is the ability to quantify accurately levels of crime across countries. Taking into account the methodological weaknesses of using crosscountry data sources, the present article provides conclusions obtained by analysing the large amount of data available from the various United Nations surveys of crime trends and operations of criminal justice systems. “Not everything that can be counted, counts. And not everything that counts can be counted.” Albert Einstein
Spoofing Face Recognition With 3D Masks
(2014)

Spoofing is the act of masquerading as a valid user by falsifying data to gain an illegitimate access. Vulnerability of recognition systems to spoofing attacks (presentation attacks) is still an open security issue in biometrics domain and among all biometric traits, face is exposed to the most serious threat, since it is particularly easy to access and reproduce. In this paper, many different types of face spoofing attacks have been examined and various algorithms have been proposed to detect them. Mainly focusing on 2D attacks forged by displaying printed photos or replaying recorded videos on mobile devices, a significant portion of these studies ground their arguments on the flatness of the spoofing material in front of the sensor. However, with the advancements in 3D reconstruction and printing technologies, this assumption can no longer be maintained. In this paper, we aim to inspect the spoofing potential of subject-specific 3D facial masks for different recognition systems and address the detection problem of this more complex attack type. In order to assess the spoofing performance of 3D masks against 2D, 2.5D, and 3D face recognition and to analyze various texture-based countermeasures using both 2D and 2.5D data, a parallel study with comprehensive experiments is performed on two data sets: the Morpho database which is not publicly available and the newly distributed 3D mask attack database.
Deep Contrast Learning for Salient Object Detection
(2016)

Salient object detection has recently witnessed substantial progress due to powerful features extracted using deep convolutional neural networks (CNNs). However, existing CNN-based methods operate at the patch level instead of the pixel level. Resulting saliency maps are typically blurry, especially near the boundary of salient objects. Furthermore, image patches are treated as independent samples even when they are overlapping, giving rise to significant redundancy in computation and storage. In this paper, we propose an end-to-end deep contrast network to overcome the aforementioned limitations. Our deep network consists of two complementary components, a pixel-level fully convolutional stream and a segment-wise spatial pooling stream. The first stream directly produces a saliency map with pixel-level accuracy from an input image. The second stream extracts segment-wise features very efficiently, and better models saliency discontinuities along object boundaries. Finally, a fully connected CRF model can be optionally incorporated to improve spatial coherence and contour localization in the fused result from these two streams. Experimental results demonstrate that our deep model significantly improves the state of the art.
FuseNet: Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture
(2016)

In this paper we address the problem of semantic labeling of indoor scenes on RGB-D data. With the availability of RGB-D cameras, it is expected that additional depth measurement will improve the accuracy. Here we investigate a solution how to incorporate complementary depth information into a semantic segmentation framework by making use of convolutional neural networks (CNNs). Recently encoder-decoder type fully convolutional CNN architectures have achieved a great success in the field of semantic segmentation. Motivated by this observation we propose an encoder-decoder type network, where the encoder part is composed of two branches of networks that simultaneously extract features from RGB and depth images and fuse depth features into the RGB feature maps as the network goes deeper. Comprehensive experimental evaluations demonstrate that the proposed fusion-based architecture achieves competitive results with the state-of-the-art methods on the challenging SUN RGB-D benchmark obtaining 76.27% global accuracy, 48.30% average class accuracy and 37.29% average intersectionover-union score.